Research

I analyze how the possibility to automate certain occupations impacts wages even in the absence of adoption of the automated technology. I build a multi-occupation search and bargaining model in which firms and workers bargain over wages and some occupations can be automated. A firm that can threaten to automate an occupation instead of hiring a worker has a higher outside option during the bargaining process. Thus, the possibility of automating improves the bargaining outcome of the firm and lowers the wage of the worker. Using data from the Current Population Survey and an index of automatability from the literature I show that, in line with the model, the threat of automation decreases workers’ wages, that this effect is more pronounced in labor markets where union intensity is higher, and that the return to experience in an occupation is affected by the threat of automation. These result suggests that, even if only a small number of firms automate occupations, new automation technologies may still have a large effect on the labor market.

Researchers in economics would often benefit from combining information from distinct datasets, for example using records from an administrative source and from survey data. This is necessary to estimate the joint distribution of variables not jointly observed in one dataset. Methods commonly used in the literature have important limitations: either they discard information by reducing the dimensionality of the matching, or they do not preserve the multivariate distributions of the variables imported form each dataset. This is especially problematic for studies using the combined dataset to construct measure of inequality. This paper details a statistical matching method using optimal transport theory that does not suffer from these drawbacks. Using data from the Current Population Survey and the IRS Public Use Files, I compare this approach to other methods. I show that the synthetic dataset built with the optimal transport matching method presents higher measures of income inequality.

We benchmark seven global and three local algorithms by comparing their performance and speed in optimizing difficult objective functions. We apply the algorithms to optimize a small suite of multidimensional test functions that are commonly used to benchmark algorithms in computational mathematics. To understand optimizers’ performance in applications that are common to economics, we apply the same optimizers to maximize the objective function of a GMM estimation problem that targets 297 moments to estimate 7 parameters. Our results show that the reliability and speed of all algorithms vary substantially depending on the dimensionality and characteristics of the problem. Experimenting with different algorithms can therefore be very helpful. We find that StoGo and Tiki-Taka Algorithm (Tiktak) are most reliable and computationally efficient in the optimization of test functions. For the economic estimation, the most reliable and efficient algorithms are Multi-Level Single-Linkage and Tiktak